title: Mean square stability of uncertain stochastic BAM neural networks with interval time-varying delays.
abstract: The robust asymptotic stability analysis for uncertain BAM neural networks with both interval time-varying delays and stochastic disturbances is considered. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges for delays, some new stability criteria are established to guarantee the delayed BAM neural networks to be robustly asymptotically stable in the mean square. Unlike the most existing mean square stability conditions for BAM neural networks, the supplementary requirements that the time derivatives of time-varying delays must be smaller than 1 are released and the lower bounds of time varying delays are not restricted to be 0. Furthermore, in the proposed scheme, the stability conditions are delay-range-dependent and rate-dependent/independent. As a result, the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples are given to illustrate the effectiveness of the proposed criteria.
authors.affiliation: College of Computer Science, Chongqing University, Chongqing, 400030 People's Republic of China ; Department of Computer Science, Chongqing Education College, Chongqing, 400067 People's Republic of China.
authors.forename: Haixia
authors.initials: H
authors.lastname: Wu
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Liao
authors.forename: Wei
authors.initials: W
authors.lastname: Feng
authors.forename: Songtao
authors.initials: S
authors.lastname: Guo
keywords: Interval time-varying delays; Linear matrix inequalities (LMIs); Stability; Stochastic disturbances; Uncertain BAM neural networks